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Electronics
Special Issues
Cyber-Physical Systems in Smart Cities
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Cyber-Physical Systems in Smart Cities

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (1 March 2022) | Viewed by 5878

Special Issue Editors

Dr. Mustafa Ilhan Akbas

E-Mail Website
Guest Editor
Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
Interests: computer engineering; cyber–physical systems; software defined networks
Special Issues, Collections and Topics in MDPI journals
Dr. Harish Chintakunta

E-Mail Website
Guest Editor
Department of Electrical Engineering, Florida Polytechnic University, Lakeland, FL 33805, USA
Interests: topological data analytics; signal processing; graph theory; social networks
Prof. Raivo Sell

E-Mail Website
Guest Editor
Autonomous Vehicles research group, Tallinn University of Technology (TalTech), 19086 Tallinn, Estonia
Interests: robotics; autonomous vehicles; self-driving shuttle; smart city
Special Issues, Collections and Topics in MDPI journals
Dr. Gürkan Solmaz

E-Mail Website
Guest Editor
NEC Laboratories Europe, 69115 Heidelberg, Germany
Interests: internet of things; mobile computing; machine learning

Special Issue Information

Dear Colleagues,

The injection of technology from the electronics industry is poised to revolutionize the urban life. There is a significant work ongoing in areas such as communication systems, connected and autonomous vehicles, Internet of Things and cloud computing. Cities of the future will use these technologies to overcome problems such as congestion, air pollution and to improve the quality of life for the urban society. Cyber-physical systems (CPS) are expected to be an integral component of these smart cities of the future. While typical and expected CPS such as connected and autonomous vehicles or delivery drones will take important roles in the urban environment, CPS such as construction robots or marine vehicles will be utilized for various purposes as well.

The aim of this Special Issue will be to feature articles on CPS and how they will impact smart cities in the future. They might span across connected and autonomous vehicle applications, unmanned aerial vehicle applications, IoT-enabled smart city features for CPS, vehicular technologies enabling CPS applications, assurance and safety mechanisms for CPS and infrastructure-level technologies to support CPS.

Within the above dimensions, the scope of the Special Issue welcomes high-quality original research and review articles that cover a broad range of topics related to CPS and smart cities. Potential topics include but are not limited to the following:

  • CPS applications in smart cities
  • Urban air mobility
  • Data collection protocols in smart cities
  • Safety Assurance of CPS
  • Protocol design for CPS in smart cities
  • Edge computing for CPS
  • Validation and Verification of CPS
  • Network management
  • Network virtualization
  • Localization and tracking
  • Crowdsensing in smart cities

Dr. Mustafa Ilhan Akbas
Dr. Harish Chintakunta
Prof. Raivo Sell
Dr. Gürkan Solmaz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • CPS applications in smart cities
  • Urban air mobility
  • Data collection protocols in smart cities
  • Safety Assurance of CPS
  • Protocol design for CPS in smart cities
  • Edge computing for CPS
  • Validation and Verification of CPS
  • Network management
  • Network virtualization
  • Localization and tracking
  • Crowdsensing in smart cities

Published Papers (2 papers)

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Research

16 pages, 1175 KiB  
Article
Safety System Assessment Case Study of Automated Vehicle Shuttle
by Heiko Pikner, Raivo Sell, Jüri Majak and Kristo Karjust
Electronics 2022, 11(7), 1162; https://doi.org/10.3390/electronics11071162 - 06 Apr 2022
Cited by 8 | Viewed by 2322
Abstract
Automated vehicle (AV) minibuses, i.e., AV shuttles, are gaining popularity in the testing of new types of transportation services in real traffic conditions. AV shuttles have moved from closed test areas to low-traffic public sites such as local residential areas, technology parks, university [...] Read more.
Automated vehicle (AV) minibuses, i.e., AV shuttles, are gaining popularity in the testing of new types of transportation services in real traffic conditions. AV shuttles have moved from closed test areas to low-traffic public sites such as local residential areas, technology parks, university campuses, etc. These types of vehicles are usually low-speed and rely on a lidar-camera sensor set and a self-driving software stack. These new use cases are increasing these systems’ safety demands. In addition to functional safety, many other aspects need to be considered. In this study, a risk analysis model is developed, combining the fuzzy analytical hierarchy process and the Technique for Order of Preference by Similarity to Ideal Solution method. The proposed model is utilized to prioritize risks corresponding to the particular case study, based on real AV shuttle bus development, and focuses on the low-level hardware/software safety issues and improvements. Full article
(This article belongs to the Special Issue Cyber-Physical Systems in Smart Cities)
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17 pages, 4444 KiB  
Article
Object Segmentation for Autonomous Driving Using iseAuto Data
by Junyi Gu, Mauro Bellone, Raivo Sell and Artjom Lind
Electronics 2022, 11(7), 1119; https://doi.org/10.3390/electronics11071119 - 01 Apr 2022
Cited by 3 | Viewed by 2524
Abstract
Object segmentation is still considered a challenging problem in autonomous driving, particularly in consideration of real-world conditions. Following this line of research, this paper approaches the problem of object segmentation using LiDAR–camera fusion and semi-supervised learning implemented in a fully convolutional neural network. [...] Read more.
Object segmentation is still considered a challenging problem in autonomous driving, particularly in consideration of real-world conditions. Following this line of research, this paper approaches the problem of object segmentation using LiDAR–camera fusion and semi-supervised learning implemented in a fully convolutional neural network. Our method was tested on real-world data acquired using our custom vehicle iseAuto shuttle. The data include all weather scenarios, featuring night and rainy weather. In this work, it is shown that with LiDAR–camera fusion, with only a few annotated scenarios and semi-supervised learning, it is possible to achieve robust performance on real-world data in a multi-class object segmentation problem. The performance of our algorithm was measured in terms of intersection over union, precision, recall, and area-under-the-curve average precision. Our network achieves 82% IoU in vehicle detection in day fair scenarios and 64% IoU in vehicle segmentation in night rain scenarios. Full article
(This article belongs to the Special Issue Cyber-Physical Systems in Smart Cities)
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